Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/66238 
Year of Publication: 
1997
Series/Report no.: 
SFB 373 Discussion Paper No. 1997,16
Publisher: 
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, Berlin
Abstract: 
Fan, Heckman and Wand (1995) proposed locally weighted kernel polynomial regression methods for generalized linear models and quasilikelihood functions. When the covariate variables are missing at random, we propose a weighted estimator based on the inverse selection probability weights. Distribution theory is derived when the selection probabilities are estimated nonparametrically. We show that the asymptotic variance of the resulting nonparametric estimator of the mean function in the main regression model is the same as that when the selection probabilities are known, while the biases are generally different. This is different from results in parametric problems, where it is known that estimating weights actually decreases asymptotic variance. To reconcile the difference between the parametric and nonparametric problems, we obtain a second-order variance result for the nonparametric case. We generalize this result to local estimating equations. Finite sample performance is examined via simulation studies. The proposed method is demonstrated via an analysis of data from a casecontrol study.
Subjects: 
local linear smoother
Generalized linear models
kernel regression
measurement error
missing at random
quasilikelihood functions
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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